• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.183-203
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• 2018

News articles are the most suitable medium for examining the events occurring at home and abroad. Especially, as the development of information and communication technology has brought various kinds of online news media, the news about the events occurring in society has increased greatly. So automatically summarizing key events from massive amounts of news data will help users to look at many of the events at a glance. In addition, if we build and provide an event network based on the relevance of events, it will be able to greatly help the reader in understanding the current events. In this study, we propose a method for extracting event networks from large news text data. To this end, we first collected Korean political and social articles from March 2016 to March 2017, and integrated the synonyms by leaving only meaningful words through preprocessing using NPMI and Word2Vec. Latent Dirichlet allocation (LDA) topic modeling was used to calculate the subject distribution by date and to find the peak of the subject distribution and to detect the event. A total of 32 topics were extracted from the topic modeling, and the point of occurrence of the event was deduced by looking at the point at which each subject distribution surged. As a result, a total of 85 events were detected, but the final 16 events were filtered and presented using the Gaussian smoothing technique. We also calculated the relevance score between events detected to construct the event network. Using the cosine coefficient between the co-occurred events, we calculated the relevance between the events and connected the events to construct the event network. Finally, we set up the event network by setting each event to each vertex and the relevance score between events to the vertices connecting the vertices. The event network constructed in our methods helped us to sort out major events in the political and social fields in Korea that occurred in the last one year in chronological order and at the same time identify which events are related to certain events. Our approach differs from existing event detection methods in that LDA topic modeling makes it possible to easily analyze large amounts of data and to identify the relevance of events that were difficult to detect in existing event detection. We applied various text mining techniques and Word2vec technique in the text preprocessing to improve the accuracy of the extraction of proper nouns and synthetic nouns, which have been difficult in analyzing existing Korean texts, can be found. In this study, the detection and network configuration techniques of the event have the following advantages in practical application. First, LDA topic modeling, which is unsupervised learning, can easily analyze subject and topic words and distribution from huge amount of data. Also, by using the date information of the collected news articles, it is possible to express the distribution by topic in a time series. Second, we can find out the connection of events in the form of present and summarized form by calculating relevance score and constructing event network by using simultaneous occurrence of topics that are difficult to grasp in existing event detection. It can be seen from the fact that the inter-event relevance-based event network proposed in this study was actually constructed in order of occurrence time. It is also possible to identify what happened as a starting point for a series of events through the event network. The limitation of this study is that the characteristics of LDA topic modeling have different results according to the initial parameters and the number of subjects, and the subject and event name of the analysis result should be given by the subjective judgment of the researcher. Also, since each topic is assumed to be exclusive and independent, it does not take into account the relevance between themes. Subsequent studies need to calculate the relevance between events that are not covered in this study or those that belong to the same subject.

• Korean Journal of Social Welfare
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• v.68 no.2
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• pp.185-211
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• 2016

This study analyzes key word networks of the papers which are published at Korean Journal of Social Welfare issued by Korean Academy of Social Welfare from 1979 to 2015. It aims at investigating the trends of social welfare researches in Korea by dividing the given period into two: 1979-2000 and 2001-2015. It shows the trends in three ways: methodologies, subjects, and intellectual structures. In order to identify intellectual structure, it calculate centrality indices basing on co-appearance frequency of key words. It also derives some values which explain relationship structure of key words by using pathfinder algorithm, and finally visualizes the intellectual structures by using the NodeXL program. Some implications of the findings of these analyses are discussed in the end.

• Journal of Information Management
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• v.43 no.2
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• pp.43-66
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• 2012

The purpose of this study is to see the intellectual structure in the field of veterinary sciences in Korea, using author profiling analysis(APA), a bibliometric approach. Three journals are selected on the basis of citation data, exchanging most citations with Korean Journal of Veterinary. And then, 50 authors who published most articles at selected journals during the given period of time were chosen. The analysis of similarity and dissimilarity among authors by comparing co-word appearance patterns from article title, abstracts, and keywords was made. Authors can be grouped 11 minor clusters under 4 major clusters, depending on their interests in the area of veterinary sciences in Korea. The subjects for each cluster at the veterinary sciences are decided by the matching the keyword, representing author's research interest. As a result, it is possible to figure out the current research trends and the researcher network in the field of veterinary sciences.

• The Journal of the Convergence on Culture Technology
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• v.8 no.4
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• pp.163-171
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• 2022

This study was conducted to understand the research trends and central concepts of middle-aged women' health in Korea. For the analysis of this study, target papers published from 2012 to 2021 were collected by entering the keywords of 'middle-aged woman' or 'menopausal woman'. 1,116 papers were used for analysis. The co-occurrence network of key words was developed and analyzed, and the research trends were analyzed through topic modeling of the LSD by dividing it into five-year units (2012-2016, 2017-2021), and visualized word cloud and sociogram were used. The keywords that appeared the most during the last 10 years were obesity, depression, body composition, stress, and menopause symptom. Five topics analyzed in the thesis data for 5 years from 2012 to 2016 were 'postmenopausal self-efficacy and satisfaction enhancement strategy', 'exercise to manage obesity and risk factors', 'intervention for obesity and stress', 'promotion of happiness and life management' and 'menopausal depression and quality of life' were confirmed. Five topics of research conducted for the next five years (2017-2021) were 'menopausal depression and quality of life', 'management of obesity and cardiovascular risk factors', 'life experience as a middle-aged woman', and 'life satisfaction and psychological well-being' and 'menopausal symptom relief strategy'. Through the results, the trend of research topics related to middle-aged women's health over the past 10 years have been identified, and research on health of middle-aged women that reflects the trend of the future should be continued.

• Knowledge Management Research
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• v.18 no.3
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• pp.81-99
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• 2017

This study is to compare the main subjects of domestic and foreign knowledge management research in terms of keywords and to clarify whether domestic knowledge management research reflects research trends in overseas knowledge management research. Specifically, we try to find out whether the central activities such as knowledge sharing, knowledge generation, and acquisition, which are knowledge management activities of knowledge management research, are being studied without bias. In order to analyze this, we analyzed the data of domestic and foreign knowledge management research for the last 5 years from 2012 to 2016. In Korea, the Knowledge Management Society of Korea collected 167 papers and 787 keywords, and collected 132 papers and 640 keywords from the Korea Society of Management Information Systems in order to distinguish the research areas. Overseas papers collected 315 papers and 1,746 keywords published by Emerald. Also, we collected 382 papers and 1,633 keywords in the Korean Management Review and collected 646 papers and 2,879 keywords in the Korean Business Education Review. Frequency analysis and network analysis of 1,642 papers and 7,685 keywords are summarized as follows. The Knowledge Management Society of Korea has focused on knowledge sharing, and in 2016, interest in knowledge transfer and knowledge search has shifted. The Journal of Knowledge Management, which is published by Emerald, has been a major concern for knowledge transfer and knowledge sharing. The research trends of the Korea Society of Management Information Systems to distinguish a clear identity of knowledge management research are focusing on smart area and mobile domain such as information security domain, cloud, smart phone, and smart work. In the Korea Society of Management Information Systems research, the main subject of knowledge sharing is also commonly found.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.30 no.2
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• pp.189-209
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• 2019

Main Path Analysis (MPA) is one of the text mining techniques that extracts the core literature that contributes knowledge transfer based on citation information in the literature. This study applied various text mining techniques to abstract of the paper related with sense-of-place, which is published at Korea and abroad from 1990 to 2018 so that could discuss in a macro perspective. The main path analysis results showed that from 1990, overseas research on sense-of-place has been carried out in the order of personal identity, public land management, environmental education and urban development-related areas. Also, by using the network analysis, this study found that sense-of-place was discussed at various levels in Korea, including urban development, culture, literature, and history. On the other hand, it has been found that there are few topic changes in international studies, and that discussions on health, identity, landscape and urban development have been going on steadily since the 1990s. This study has implications that it presents a new perspective of grasping the overall flow of relevant research.

• Journal of Digital Convergence
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• v.16 no.11
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• pp.1-15
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• 2018

The purpose of this study is to analyze bibliographic information of Busan and other cities' reports for urban development initiative and identify the strategic fields for future smart city plan. Text mining method is used in this study to extract keywords and identify the characteristics and patterns of information in urban development reports. As a result, in earlier stage, Busan city focused on service creation for industrial development but there are lack of discussions on the linkage of information systems with ICT technology. However, recent urban planning in Busan contained various contents related to integrated connections of infrastructure, ICT system, and operation management of city in the specific fields of traffic, tourism, welfare, port/logistics, culture/MICE. This results of study is expected to provide policy implications for planning the future urban initiatives of smart city development.

• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.137-147
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• 2022

Diagnosis and management of skin condition is a very basic and important function in performing its role for workers in the beauty industry and cosmetics industry. For accurate skin condition diagnosis and management, it is necessary to understand the skin condition and needs of customers. In this paper, we developed SCIS, a big data-based skin care information system that supports skin condition diagnosis and management using social media big data for skin condition diagnosis and management. By using the developed system, it is possible to analyze and extract core information for skin condition diagnosis and management based on text information. The skin care information system SCIS developed in this paper consists of big data collection stage, text preprocessing stage, image preprocessing stage, and text word analysis stage. SCIS collected big data necessary for skin diagnosis and management, and extracted key words and topics from text information through simple frequency analysis, relative frequency analysis, co-occurrence analysis, and correlation analysis of key words. In addition, by analyzing the extracted key words and information and performing various visualization processes such as scatter plot, NetworkX, t-SNE, and clustering, it can be used efficiently in diagnosing and managing skin conditions.

• Journal of the Korean Society for information Management
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• v.40 no.4
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• pp.429-450
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• 2023

The purpose of this study is to examine recent trends and intellectual structures in research related to open data. To achieve this, the study conducted a search for the keyword "open data" in Scopus and collected a total of 6,543 papers from 1999 to 2023. After data preprocessing, the study focused on the author keywords of 5,589 papers to perform network analysis and derive centrality in the field of open data research and linked open data research. As a result, the study found that "big data" exhibited the highest centrality in research related to open data. The research in this area mainly focuses on the utilization of open data as a concept of public data, studies on the application of open data in analysis related to big data as an associated concept, and research on topics related to the use of open data, such as the reproduction, utilization, and access of open data. In linked open data research, both triadic centrality and closeness centrality showed that "the semantic web" had the highest centrality. Moreover, it was observed that research emphasizing data linkage and relationship formation, rather than public data policies, was more prevalent in this field.

• Journal of the Korean Society for information Management
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• v.35 no.3
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• pp.11-39
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• 2018

The study examines the research trends of doctoral dissertations in Library Science and Information Science published in the U.S. for the last 5 years. Data collected from PQDT Global includes 1,016 doctoral dissertations containing "Library Science" or "Information Science" as subject headings, and keywords extracted from those dissertations were used for a network analysis, which helps identifying the intellectual structure of the dissertations. Also, the analysis using 103 subject heading keywords resulted in various centrality measures, including triangle betweenness centrality and nearest neighbor centrality, as well as 26 clusters of associated subject headings. The most frequently studied subjects include computer-related subjects, education-related subjects, and communication-related subjects, and a cluster with information science as the most central subject contains most of the computer-related keywords, while a cluster with library science as the most central subject contains many of the education-related keywords. Other related subjects include various user groups for user studies, and subjects related to information systems such as management, economics, geography, and biomedical engineering.